Embracing AI to improve patient care and advance innovation in pharma (Guest blog)
Artificial Intelligence (AI) in its many forms has been around for years and has only recently started to show real value for pharma. In the light of the opportunities and challenges raised by the increased pace in adopting and deploying AI solutions, we are excited to see the publications of the EU Pharmaceutical Strategy and the EFPIA Position Paper on Artificial Intelligence.
The EU Pharmaceutical Strategy includes an important focus on how digital transformation including the use of high-performance computing and AI will be an enabler for innovation and applied for e.g. prevention, diagnosis, better treatment, therapeutic monitoring and data for personalised medicines and many other healthcare applications.
The definition of AI adopted by EFPIA as a “problem-solving tool” is welcome and very important, as pharma companies need to focus on what is representative and relevant for a patient-centric approach. AI should be appropriately used to advance the design and delivery of healthcare for the benefit of patients and healthcare professionals in a manner that allows human oversight. This is the best way to ensure that transparency and ethical principles are core to our AI solutions and by employing AI, we could improve our disease understanding, fast-track drug discovery and accelerate clinical trials to help us bring treatments faster to those who need them.
AI also has a role on the process efficiency side, which is equally important to delivering on the EFPIA vision “to identify, treat and care for patients more efficiently, while preserving patient safety”. AI applications employing technologies like deep learning (DL), machine learning (ML) and natural language processing (NLP) have the potential to improve our workplaces, streamline existing processes and improve collaboration. Examples of projects include: applying DL and ML to drug discovery and clinical trial optimization, employing NLP to process unstructured content such as scientific publications, patents, and public databases, adapting open source algorithms developed by academia and leading technology companies to build “smart libraries” of historic content and insights. Adopting AI into the ecosystem will result in gaps and overlaps in existing guidelines and regulations. It’s essential regulation of AI is simple and application doable to facilitate innovation while at the same time being trustworthy and consistent. Clarification of the role of AI in association with existing guidelines e.g. Medical Devices Regulation (MDR) is much preferable rather than creating new rules.
There are three foundational elements for successfully implementing AI: data, cloud access and data skills:
Data. In order to fully harness the power of AI, we need to start with the quality and availability of data. As advocated by EFPIA, access to high-quality data is indispensable and it comes hand in hand with developing transparent and solid data governance processes in line with legal and compliance requirements.
Cloud access. The coronavirus crisis has accelerated digital adoption in multiple business settings, transforming the way we interact with different parts of the healthcare system, while opening the potential to adopt more agile ways of working. To respond to the increasing need for speed and computing capacity, access to cloud and similar technology platforms is a prerequisite for rapid prototyping of data experiments and use cases, as well as a requirement to scale up these successful innovations at enterprise level.
Data skills. Supporting the development and acquisition of new skills and capabilities across the organisation is, with no surprise, one of the most important elements of delivering a successful data and AI strategy. In fact, the EFPIA paper explains in detail how AI competencies are an enabler to the strategy. This involves not only allowing existing pools of skills and capabilities in data to grow and apply their knowledge in new areas, but also a general effort to increase the data literacy and awareness of the entire organisation.
On an industry level, collaboration and transparency are needed to fulfil the potential of AI. To make progress while the highest ethical frameworks are respected, all the members of the ecosystem need to contribute. The healthcare system is uniquely positioned to identify and align on best practices in employing AI-powered applications, to share learnings and harness the power of collaborations between healthcare professionals, academia, start-ups and pharma companies.
EFPIA is well placed to facilitate and encourage this exchange and collaboration, so that the whole AI ecosystem progresses effectively.
You can read the full EFPIA Position on AI here.